An interpretable machine learning model based on computed tomography radiomics for predicting programmed death ligand 1 expression status in gastric cancer.

Journal: Cancer imaging : the official publication of the International Cancer Imaging Society
PMID: 40075494

Abstract

BACKGROUND: Programmed death ligand 1 (PD-L1) expression status, closely related to immunotherapy outcomes, is a reliable biomarker for screening patients who may benefit from immunotherapy. Here, we developed and validated an interpretable machine learning (ML) model based on contrast-enhanced computed tomography (CECT) radiomics for preoperatively predicting PD-L1 expression status in patients with gastric cancer (GC).

Authors

  • Lihuan Dai
    Department of Medical Imaging, Guangzhou Institute of Cancer Research, the Affiliated Cancer Hospital, Guangzhou Medical University, Guangzhou, 510095, China.
  • Jinxue Yin
    Department of Medical Imaging, Guangzhou Institute of Cancer Research, the Affiliated Cancer Hospital, Guangzhou Medical University, Guangzhou, 510095, China.
  • Xin Xin
  • Chun Yao
    Department of Radiology, Meizhou People's Hospital, Mei Zhou, 514031, China.
  • Yongfang Tang
    Department of Medical Imaging, Guangzhou Institute of Cancer Research, the Affiliated Cancer Hospital, Guangzhou Medical University, Guangzhou, 510095, China.
  • Xiaohong Xia
    Department of Pathology, Guangzhou Institute of Cancer Research, the Affiliated Cancer Hospital, Guangzhou Medical University, Guangzhou, 510095, China.
  • Yuanlin Chen
    Department of Pathology, Guangzhou Institute of Cancer Research, the Affiliated Cancer Hospital, Guangzhou Medical University, Guangzhou, 510095, China.
  • Shuying Lai
    Department of Radiology, Meizhou People's Hospital, Mei Zhou, 514031, China.
  • Guoliang Lu
    Department of Medical Imaging, Guangzhou Institute of Cancer Research, the Affiliated Cancer Hospital, Guangzhou Medical University, Guangzhou, 510095, China.
  • Jie Huang
    Department of Critical Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
  • Purong Zhang
    Department of Medical Imaging, Guangzhou Institute of Cancer Research, the Affiliated Cancer Hospital, Guangzhou Medical University, Guangzhou, 510095, China.
  • Jiansheng Li
    Department of Medical Imaging, Guangzhou Institute of Cancer Research, the Affiliated Cancer Hospital, Guangzhou Medical University, Guangzhou, 510095, China. lijiansheng@gzhmu.edu.cn.
  • Xiangguang Chen
    Department of Radiology, Meizhou People's Hospital, Mei Zhou, 514031, China. cxg966504@163.com.
  • Xi Zhong
    Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.

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